Immune monitoring using the predictive power of immune profiles

Abstract

BackgroundWe have developed a novel approach to categorize immunity in patients that uses a combination of whole blood flow cytometry and hierarchical clustering.MethodsOur approach was based on determining the number (cells/μl) of the major leukocyte subsets in unfractionated, whole blood using quantitative flow cytometry. These measurements were performed in 40 healthy volunteers and 120 patients with glioblastoma, renal cell carcinoma, non-Hodgkin lymphoma, ovarian cancer or acute lung injury. After normalization, we used unsupervised hierarchical clustering to sort individuals by similarity into discreet groups we call immune profiles.ResultsFive immune profiles were identified. Four of the diseases tested had patients distributed across at least four of the profiles. Cancer patients found in immune profiles dominated by healthy volunteers showed improved survival (p < 0.01). Clustering objectively identified relationships between immune markers. We found a positive correlation between the number of granulocytes and immunosuppressive CD14+HLA-DRlo/neg monocytes and no correlation between CD14+HLA-DRlo/neg monocytes and Lin-CD33+HLA-DR- myeloid derived suppressor cells. Clustering analysis identified a potential biomarker predictive of survival across cancer types consisting of the ratio of CD4+ T cells/μl to CD14+HLA-DRlo/neg monocytes/μL of blood.ConclusionsComprehensive multi-factorial immune analysis resulting in immune profiles were prognostic, uncovered relationships among immune markers and identified a potential biomarker for the prognosis of cancer. Immune profiles may be useful to streamline evaluation of immune modulating therapies and continue to identify immune based biomarkers.

Intratumoral CD14+ Cells and Circulating CD14+HLA-DRlo/neg Monocytes Correlate with Decreased Survival in Patients with Clear Cell Renal Cell Carcinoma

“…Three patients had papillary histology. The clinical characteristics of the RCC patients have been described elsewhere (11). All studies were performed under the approval of the Mayo Clinic Institutional Review Board.…”

“…Quantitative whole blood flow cytometry and data analysis, including hierarchical clustering, for the patient and healthy volunteer samples were previously described (11). Additional principle component analysis of flow data was performed in Partek Genomics Suite 6.6 (Partek Inc.).…”

“…Recently, we used quantitative whole blood flow cytometry on 160 healthy volunteers and patients from four cancer types, including RCC, to identify immune profiles of cancer patients that were prognostic for overall survival (11). We took a comprehensive approach by measuring all of the major leukocyte populations, including several immunosuppressive cell types such as regulatory T cells and myeloid-derived suppressor cells (MDSC).…”

Immune independent crosstalk between lymphoma and myeloid suppressor CD14+HLA-DRlow/negmonocytes mediates chemotherapy resistance

“…Similar to studies in other cancer types, [8][9][10][11][12] we found that increased presence of CD14 C HLA-DR low/neg monocytes in lymphoma correlated with increased disease burden, worse treatment response and decreased survival. 5,13 In addition, decreased presence of these cells is associated with improved clinical response to treatments. 12,14 In this study, we examined lymphoma and monocyte crosstalk that promote lymphoma survival.…”

A comparison of DNA methylation specific droplet digital PCR (ddPCR) and real time qPCR with flow cytometry in characterizing human T cells in peripheral blood

“…15 Other studies also have reported that low T cell counts were associated with poorer glioma survival times. 16,17 More generally, T cell lymphopenia has been shown to be a grave prognostic indicator in multiple types of cancers. [18][19][20][21][22][23] Recently, digital PCR platforms have been developed that offer advantages over real time qPCR including the capability to obtain absolute quantification without external references and to be highly robust to variations in PCR efficiency, thus improving assay precision.…”